Comparar métodos

	Análisis Envolvente de Datos de Súper-Eficiencia	Bootstrap DEA	Análisis Envolvente de Datos en Red (Network DEA)
Campo	Análisis de eficiencia	Análisis de eficiencia	Análisis de eficiencia
Familia	Regression model	Regression model	Regression model
Año de origen	1993	1998	2000
Autor original	Andersen & Petersen	Simar & Wilson	Färe & Grosskopf
Tipo	Nonparametric linear programming model	Nonparametric efficiency estimation with bootstrap inference	Multi-stage nonparametric efficiency model
Fuente seminal	Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261–1264.	Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61.	Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49.
Alias	Andersen-Petersen Model, Super-Radial DEA, Ranking DEA, Süper Etkinlik VZA	Bootstrapped DEA, DEA Bootstrap Inference, Simar-Wilson Bootstrap, Bootstrap Sınır Analizi	Network Data Envelopment Analysis, Network Efficiency Analysis, Multi-Stage DEA, Ağ Veri Zarflama Analizi
Relacionados	2	2	2
Resumen	Super-Efficiency DEA is a nonparametric linear programming extension of classical Data Envelopment Analysis (DEA) introduced by Andersen and Petersen (1993). While standard DEA assigns a maximum efficiency score of 1.0 to all units on the efficient frontier, Super-Efficiency DEA allows efficient units to receive scores greater than 1.0 by temporarily removing the evaluated unit from the reference set. This modification enables full ranking of all decision-making units (DMUs), including those previously indistinguishable at the frontier.	Bootstrap Data Envelopment Analysis (Bootstrap DEA) is a resampling-based extension of standard DEA that provides statistically valid inference for efficiency scores. Introduced by Simar and Wilson in 1998, it addresses the core weakness of classical DEA — its inability to quantify uncertainty in estimated scores — by constructing bootstrap confidence intervals and bias-corrected efficiency estimates from repeatedly resampled pseudo-frontiers.	Network Data Envelopment Analysis (Network DEA) is a nonparametric efficiency measurement framework introduced by Färe and Grosskopf (2000) that extends classical DEA to multi-stage or multi-division production processes. Rather than treating a decision-making unit as a black box, it explicitly models the internal structure — the divisions and the intermediate products that flow between them — enabling stage-level and overall efficiency scores to be estimated simultaneously within a single coherent model.